Spatial-spectrum estimation in a location sector

Multiple narrowband source localization using arbitrarily configured arrays and spatial-spectrum estimation is considered. A new eigenspace-based approach which uses projections onto a particular vector or vector set in the estimated noise-only subspace is described. Several CLOSEST vector estimators are developed by using different measures of closeness. First CLOSEST is a novel full-dimensional element-space approach to spatial-spectrum estimation which has important performance advantages relative to pertinent established spatial-spectrum estimators. It incorporates a priori knowledge of the array manifold over a location sector of interest to provide SNR spectral-resolution thresholds which are lower than those of MIN-NORM (for some arrays, substantially lower). Second, relationships between the CLOSEST approach and several established approaches to spatial-spectrum estimation are established. For a linear equispaced array, MIN-NORM is shown to be a special case of the CLOSEST-approach-one which is based on projection onto a noise-only subspace vector which is close to the array manifold over the entire field of view